{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cogms.utils_pydmc import Plot\n",
    "from cogms.simulators import experimenti_simulator, DMC_trial, SSP_trial, DSTP_trial\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DMC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(396.0, 1.0)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "peak = 20\n",
    "tau = 30\n",
    "vc = 0.5\n",
    "a = 90*2  # 70\n",
    "ndt = 300\n",
    "alpha = 2\n",
    "\n",
    "dc = 4\n",
    "dt = 1\n",
    "\n",
    "DMC_trial((a,ndt,vc,peak,alpha,tau,0.5),-1, dc=dc,dt=dt)[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Epool\\AppData\\Local\\Temp\\ipykernel_39116\\3278337716.py:1: DeprecationWarning: Call to deprecated function experimenti_simulator. Use dmc_experimenti_simulator instead.\n",
      "  df = experimenti_simulator((a,ndt,vc,peak,alpha,tau,0.5), DMC_trial, n_samples=1000, n_subjects=10, sim_kwargs={\"dt\":dt,\"dc\":dc})\n"
     ]
    }
   ],
   "source": [
    "df = experimenti_simulator((a,ndt,vc,peak,alpha,tau,0.5), DMC_trial, n_samples=1000, n_subjects=10, sim_kwargs={\"dt\":dt,\"dc\":dc})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"rt\"] = df[\"rt\"]/1000\n",
    "m_plot = Plot(df)\n",
    "m_plot.delta()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Epool\\AppData\\Local\\Temp\\ipykernel_39116\\1930385149.py:11: DeprecationWarning: Call to deprecated function experimenti_simulator. Use dmc_experimenti_simulator instead.\n",
      "  df = experimenti_simulator(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a_new is 1.4230249470757705; t_new is 0.3; vc_new is 3.952847075210474; peak_new is 158.11388300841895\n"
     ]
    }
   ],
   "source": [
    "dc_old = 4\n",
    "dt_old = 1\n",
    "dc_new = 1\n",
    "dt_new = 0.001\n",
    "\n",
    "a_new = a *(dc_new/dc_old)*(np.sqrt(dt_new/dt_old))\n",
    "t_new = ndt * dt_new\n",
    "vc_new = vc *(dc_new/dc_old)*(np.sqrt(dt_old/dt_new))\n",
    "peak_new = peak * (dc_new/dc_old)*(np.sqrt(dt_old/dt_new))\n",
    "\n",
    "df = experimenti_simulator(\n",
    "    (\n",
    "        a_new,\n",
    "        t_new,\n",
    "        vc_new,\n",
    "        peak_new,\n",
    "        alpha,\n",
    "        tau,\n",
    "        0.5\n",
    "    ), \n",
    "    DMC_trial, \n",
    "    n_samples=1000, n_subjects=10, \n",
    "    sim_kwargs={\"dt\":dt_new,\"dc\":dc_new})\n",
    "\n",
    "print(f\"a_new is {a_new}; t_new is {t_new}; vc_new is {vc_new}; peak_new is {peak_new}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m_plot = Plot(df)\n",
    "m_plot.delta()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DSTP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(396.0, 1.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "peak = 20\n",
    "tau = 30\n",
    "vc = 0.5\n",
    "a = 90*2  # 70\n",
    "ndt = 300\n",
    "alpha = 2\n",
    "\n",
    "dc = 4\n",
    "dt = 1\n",
    "\n",
    "DMC_trial((a,ndt,vc,peak,alpha,tau,0.5),-1, dc=dc,dt=dt)[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Epool\\AppData\\Local\\Temp\\ipykernel_39116\\3278337716.py:1: DeprecationWarning: Call to deprecated function experimenti_simulator. Use dmc_experimenti_simulator instead.\n",
      "  df = experimenti_simulator((a,ndt,vc,peak,alpha,tau,0.5), DMC_trial, n_samples=1000, n_subjects=10, sim_kwargs={\"dt\":dt,\"dc\":dc})\n"
     ]
    }
   ],
   "source": [
    "df = experimenti_simulator((a,ndt,vc,peak,alpha,tau,0.5), DMC_trial, n_samples=1000, n_subjects=10, sim_kwargs={\"dt\":dt,\"dc\":dc})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"rt\"] = df[\"rt\"]/1000\n",
    "m_plot = Plot(df)\n",
    "m_plot.delta()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Epool\\AppData\\Local\\Temp\\ipykernel_39116\\1930385149.py:11: DeprecationWarning: Call to deprecated function experimenti_simulator. Use dmc_experimenti_simulator instead.\n",
      "  df = experimenti_simulator(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a_new is 1.4230249470757705; t_new is 0.3; vc_new is 3.952847075210474; peak_new is 158.11388300841895\n"
     ]
    }
   ],
   "source": [
    "dc_old = 4\n",
    "dt_old = 1\n",
    "dc_new = 1\n",
    "dt_new = 0.001\n",
    "\n",
    "a_new = a *(dc_new/dc_old)*(np.sqrt(dt_new/dt_old))\n",
    "t_new = ndt * dt_new\n",
    "vc_new = vc *(dc_new/dc_old)*(np.sqrt(dt_old/dt_new))\n",
    "peak_new = peak * (dc_new/dc_old)*(np.sqrt(dt_old/dt_new))\n",
    "\n",
    "df = experimenti_simulator(\n",
    "    (\n",
    "        a_new,\n",
    "        t_new,\n",
    "        vc_new,\n",
    "        peak_new,\n",
    "        alpha,\n",
    "        tau,\n",
    "        0.5\n",
    "    ), \n",
    "    DMC_trial, \n",
    "    n_samples=1000, n_subjects=10, \n",
    "    sim_kwargs={\"dt\":dt_new,\"dc\":dc_new})\n",
    "\n",
    "print(f\"a_new is {a_new}; t_new is {t_new}; vc_new is {vc_new}; peak_new is {peak_new}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m_plot = Plot(df)\n",
    "m_plot.delta()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pymc5_3.11",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "undefined.undefined.undefined"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
